Motion Artifact Suppression Research Articles

Dynamic Peripheral Hemoperfusion Distribution Monitoring Based on Janus Flexible Sensor System.

Peripheral vascular disease is a worldwide leading health concern. Real-time peripheral hemoperfusion monitoring during treatment is essential to plan treatment strategies to improve circulatory enhancement effects. The present work establishes a Janus flexible perfusion (JFP) sensor system for dynamic peripheral hemoperfusion monitoring. We develop a Janus structure with different Young's modulus to improve the mechanical properties for motion artifacts suppression. Besides, we propose a peripheral perfusion index (PPI) based on an optical perfusion model that is experimentally verified using an in-vitro model. The effectiveness of the system is assessed in three experimental scenarios, including motion artifact-robust test, induced vascular occlusion, and peripheral hemoperfusion monitoring with the intermittent pneumatic compression treatment. The noise level of the traditional rigid sensor is five times that of the JFP sensor within the effective signal frequency domain when there is movement. The PPI can effectively discriminate between different peripheral hemoperfusion states and has a correlation coefficient of 0.92 with the Laser Doppler flowmetry (LDF) mean values. The kappa statistic between the JFP sensor and LDF is 0.78, indicating substantial agreement to estimate the peripheral hemoperfusion improvements. The sensor system we proposed can monitor peripheral hemoperfusion variation in real-time and is insensitive to motion artifacts. The proposed sensing system provides a functional module for real-time estimation of peripheral hemoperfusion during clinical interventions.

Real-time motion artifact suppression using convolution neural networks with penalty in fNIRS.

Removing motion artifacts (MAs) from functional near-infrared spectroscopy (fNIRS) signals is crucial in practical applications, but a standard procedure is not available yet. Artificial neural networks have found applications in diverse domains, such as voice and image processing, while their utility in signal processing remains limited. In this work, we introduce an innovative neural network-based approach for online fNIRS signals processing, tailored to individual subjects and requiring minimal prior experimental data. Specifically, this approach employs one-dimensional convolutional neural networks with a penalty network (1DCNNwP), incorporating a moving window and an input data augmentation procedure. In the training process, the neural network is fed with simulated data derived from the balloon model for simulation validation and semi-simulated data for experimental validation, respectively. Visual validation underscores 1DCNNwP's capacity to effectively suppress MAs. Quantitative analysis reveals a remarkable improvement in signal-to-noise ratio by over 11.08 dB, surpassing the existing methods, including the spline-interpolation, wavelet-based, temporal derivative distribution repair with a 1 s moving window, and spline Savitzky-Goaly methods. Contrast-to-noise ratio (CNR) analysis further demonstrated 1DCNNwP's ability to restore or enhance CNRs for motionless signals. In the experiments of eight subjects, our method significantly outperformed the other approaches (except offline TDDR, t < -3.82, p < 0.01). With an average signal processing time of 0.53 ms per sample, 1DCNNwP exhibited strong potential for real-time fNIRS data processing. This novel univariate approach for fNIRS signal processing presents a promising avenue that requires minimal prior experimental data and adapts seamlessly to varying experimental paradigms.

Cross-wavelength calibrating method for real-time imaging of tissue optical properties using frequency-domain diffuse optical spectroscopy.

Frequency-domain diffuse optical spectroscopy (FD-DOS) is a powerful non-invasive technique for assessing tissue optical properties, with applications ranging from basic research to clinical diagnosis. In this study, we introduce and validate a novel approach termed the cross-wavelength calibrating (CWC) method within the framework of TrackDOSI, a real-time FD-DOS imaging system for tissue characterization. The CWC method aims to mitigate the effects of changing optical coupling and motion artifacts encountered during probe scanning, thus enhancing the accuracy and reliability of optical property measurements. Notably, the CWC method also allows for a simpler geometry with fewer sources than traditional self-calibrating (SC) methods, reducing instrumental complexity and cost while maintaining robustness in estimating optical properties. We first validate the CWC method on solid silicone phantoms, demonstrating strong agreement with the gold standard SC method with an error of -10% and 1% for absorption and reduced scattering coefficients, respectively. Furthermore, experiments on phantom and human tissue reveal the CWC approach's ability to suppress motion artifacts and optical coupling variations, thereby improving measurement repeatability, signal fidelity, and artifact correction in dynamic imaging scenarios. Our findings underscore the potential of the CWC method to enhance the clinical utility of DOSI techniques by enabling real-time artifact correction and improving the accuracy of tissue optical property measurements.

Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function

Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have been based on directly applying existing models, and the trained models are rarely accessible. Therefore, we aim to develop and evaluate a deep-learning-based method (Motion Correction-Net, or MC-Net) for suppressing motion artifacts in brain MRI scans. Methods: A total of 57 subjects, providing 20,889 slices in four datasets, were used. Furthermore, 3T 3D sagittal magnetization-prepared rapid gradient-echo (MP-RAGE) and 2D axial fluid-attenuated inversion-recovery (FLAIR) sequences were acquired. The MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network to remove motion artifacts. Evaluation used simulated T1- and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans. The performance indices included the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and visual reading scores from three blinded clinical readers. A one-sided Wilcoxon signed-rank test was used to compare reader scores, with p &lt; 0.05 considered significant. Intraclass correlation coefficients (ICCs) were calculated for inter-rater evaluations. Results: The MC-Net outperformed other methods in terms of PSNR and SSIM for the T1 axial test set. The MC-Net significantly improved the quality of all T1-weighted images for all directions (i.e., the mean SSIM of axial, sagittal, and coronal slices improved from 0.77, 0.64, and 0.71 to 0.92, 0.75, and 0.84; the mean PSNR improved from 26.35, 24.03, and 24.55 to 29.72, 24.40, and 25.37, respectively) and for simulated as well as real motion artifacts, both using quantitative measures and visual scores. However, MC-Net performed poorly for images with untrained T2-weighted contrast because the T2 contrast was unseen during training and is different from T1 contrast. Conclusion: The proposed two-stage multi-loss MC-Net can effectively suppress motion artifacts in brain MRI without compromising image quality. Given the efficiency of MC-Net (with a single-image processing time of ~40 ms), it can potentially be used in clinical settings.

Motion-robust free-running volumetric cardiovascular MRI.

To present and assess an outlier mitigation method that makes free-running volumetric cardiovascular MRI (CMR) more robust to motion. The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics-guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm. For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies. Then, CORe is compared to CS using seven three-dimensional (3D) cine, 12 rest four-dimensional (4D) flow, and eight stress 4D flow imaging datasets. Our simulation studies show that CORe outperforms CS, RR, and the existing outlier rejection method in terms of normalized mean square error and structural similarity index across 55 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. Finally, 4D flow images show that CORe yields more reliable and consistent flow measurements, especially in the presence of involuntary subject motion or exercise stress. An outlier rejection method is presented and tested using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications.

Wearable EMG Measurement Device Using Polyurethane Foam for Motion Artifact Suppression.

We propose the use of a specially designed polyurethane foam with a plateau region in its mechanical characteristics-where stress remains nearly constant during deformation-between the electromyography (EMG) electrode and clothing to suppress motion artifacts in EMG measurement. Wearable EMG devices are receiving attention for monitoring muscle weakening due to aging. However, daily EMG measurement has been challenging due to motion artifacts caused by changes in the contact pressure between the bioelectrode and the skin. Therefore, this study aims to measure EMG signals in daily movement environments by controlling the contact pressure using polyurethane foam between the bioelectrode on the clothing and the skin. Through mechanical calculations and finite element method simulations of the polyurethane foam's effect, we clarified that the characteristics of the polyurethane foam significantly influence contact pressure control and that the contact pressure is adjustable through the polyurethane foam thickness. The optimization of the design successfully controlled the contact pressure between the bioelectrode and skin from 1.0 kPa to 2.0 kPa, effectively suppressing the motion artifact in EMG measurement.

An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging

Motion artifacts are a major challenge in the in vivo application of catheter-based cardiac imaging modalities. Gating is a critical tool for suppressing motion artifacts. Electrocardiogram (ECG) gating requires a trigger device or synchronous ECG recordings for retrospective analysis. Existing retrospective software gating methods extract gating signals through separate steps based on changes in vessel morphology or image features, which require a high computational cost and are prone to error accumulation. In this paper, we report on an end-to-end unsupervised learning framework for retrospective image-based gating (IBG) of catheter-based intracoronary images, named IBG Network. It establishes a direct mapping from a continuously acquired image sequence to a gated subsequence. The network was trained on clinical data sets in an unsupervised manner, addressing the difficulty of obtaining the gold standard in deep learning-based motion suppression techniques. Experimental results of in vivo intravascular ultrasound and optical coherence tomography sequences show that the proposed method has better performance in terms of motion artifact suppression and processing efficiency compared with the state-of-the-art nonlearning signal-based and IBG methods.

Research on the Innovation of Teaching Mode of Orienteering Sports Courses in Colleges and Universities Based on Mobile Intelligent Terminals

Abstract This paper studied motion state recognition algorithms using acceleration signals (ACC). At the same time, research is also being conducted on ECG motion artifact suppression algorithms and respiration rate detection algorithms. Based on this, we designed a mobile intelligent terminal sports app that allows users to view physiological signals and sports status in real time at any time. In college and university orienteering classes, the mobile intelligent terminal movement app is used to teach the sport. The results of comparing the experimental group and the control group in the orienteering course show that the mobile intelligent terminal orienteering teaching makes a significant difference in the men’s physical quality, except for the seated forward body flexion (P&lt;0.05). There were significant differences in the women’s group in terms of 50 m running (P = 0.013) and 800 m (P = 0.042). The experiment improved the dimensions of the Orienteering Campus Short Distance Test, Learning Motivation Indicator, and Emotional Management.

An 8-Channel Ambulatory EEG Recording IC With In-Channel Fully-Analog Real-Time Motion Artifact Extraction and Removal.

Wereport the design, implementation, and experimental characterization of an 8-channel EEG recording IC (0.13 μm CMOS, 12 mm 2 total area) with a channel architecture that conducts both the extraction and removal of motion artifacts on-chip and in-channel. The proposed dual-path feed-forward method for artifact extraction and removal is implemented in the analog domain, hence is needless of a DSP unit for artifact estimation, and its associated high-DR ADCs and DACs employed by the state of the art for artifact replica generation. Additionally, the presented architecture improves system's scalability as it enables channels' stand-alone operation, and yields the lowest reported channel power consumption among works featuring motion artifact detection/removal. Following an experimental study on electrode-skin interface electrical characteristics for dry electrodes in the absence and presence of motions, the article presents the channel architecture, its detailed signal transfer function analysis, circuit-level implementation, and experimental characterization results. Our measurement results show an amplification voltage gain of 48.3 dB, a bandwidth of 300 Hz, rail-to-rail input DC offset tolerance, and 41.5 dB artifact suppression, while consuming 55 μW per channel. The system's efficacy in EEG motion artifact suppression is validated experimentally, and system- and circuit-level features and performance metrics of the presented design are compared with the state of the art.

Detector-trigger-based cardiac multiphase micro-CT imaging for small animals.

Micro-computed tomography is important in cardiac imaging for preclinical small animal models, but motion artifacts may appear due to the rapid heart rates. To avoid influence of motion artifacts, the prospective ECG gating schemes based on an X-ray source trigger have been investigated. However, due to the lack of pulsed X-ray exposure modes, high-resolution micro-focus X-ray sources do not support source triggering in most cases. To develop a fast-cardiac multiphase acquisition strategy using prospective ECG gating for micro-focus X-ray tubes with a continuous emission mode. The proposed detector-trigger-based prospective ECG gating acquisition scheme (DTB-PG) triggers the X-ray detector at the R peak of ECG, and then collects multiple phase projections of the heart in one ECG cycle by sequence acquisition. Cardiac multiphase images are reconstructed after performing the same acquisition in all views. The feasibility of this strategy was verified in multiphase imaging experiments of a phantom with 150 ms motion period and a mouse heart on a micro-focus micro-CT system with continuous emission mode. Using a high frame-rate CMOS detector, DTB-PG discriminates the positions of the motion phantom well in 10 different phases and enables to distinguish the changes in the cardiac volume of the mouse in different phases. The acquisition rate of DTB-PG is much faster than other prospective gating schemes as demonstrated by theoretical analysis. DTB-PG combines the advantages of prospective ECG gating strategies and X-ray detector-trigger mode to suppress motion artifacts, achieve ultra-fast acquisition rates, and relax hardware limitations.

Motion Artifact Suppression Method for the Clinical Application of Otoscopic Spectral-Domain Optical Coherence Tomography

A compact OCT device and a method for image reconstruction are described. The proposed algorithm contains a novel procedure for motion artifact suppression based on a correction of the phase of the original interferometric signal due to the mutual correlation of adjacent A-scans. This procedure corrects distortions caused by unwanted displacements of the probe relative to the object in real time at a speed of up to 3 mm/s and an image acquisition rate of 20 B-scans per second. All processing is performed in real time using only the CPU, allowing the device to be controlled from a consumer-grade laptop or compact PC without the need for a discrete GPU. Due to its compact size, the device can be used in the conditions of an ENT examination room or operating room and can be freely moved to another room without the help of additional personnel, if necessary.

Adaptive Algorithm for Motion Artifacts Removal in Wearable Biomedical Sensors During Physical Exercise

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Motion artifacts (MA) are significant sources of noise in the wearable devices that are used to collect biological information from the human body. Photoplethysmography (PPG) is an example of the sensors embedded in many of these devices. PPG signals are highly influenced by MA, especially when the motion involves the organ that the PPG sensor is attached to. The aim of this paper is to build an adaptive algorithm and multiresolution analysis technique to denoise the raw PPG signals, suppress the MAs and accurately estimate the HR as an example of MA suppression in the wearable devices. The challenge here is to find the right selection considering high accuracy and low-computational complexity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> Discrete wavelet transform recursive inverse (DWT-RI) adaptive filter algorithm in addition to multiresolution analysis is implemented to suppress MAs and estimate the HR using the simultaneously recorded accelerations in the three dimensions as reference signals for the adaptive algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The proposed algorithm has a low computational cost and results in an absolute average error of 1.17 beats per minute (BPM) when tested on 12 subjects from a well-known database for this purpose. The performance of the proposed algorithm is compared to the other existing methods in literature used for noise removal and motion artifact suppression. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> DWT-RI adaptive algorithm can successfully suppress motion artifacts in PPG signals with a low computational complexity and outperforms several alternatives in terms of the average absolute error.

Influence of Medial Patellofemoral Ligament Reconstruction on Patellofemoral Contact in Patients With Low-Flexion Patellar Instability: An MRI Study.

Medial patellofemoral ligament (MPFL) reconstruction is a well-established procedure for the treatment of patients with patellofemoral instability (PFI) at low flexion angles (0°-30°). Little is known about the effect of MPFL surgery on patellofemoral cartilage contact area (CCA) during the first 30° of knee flexion. The purpose of this study was to investigate the effect of MPFL reconstruction on CCA using magnetic resonance imaging (MRI). We hypothesized that patients with PFI would have a lower CCA than patients with healthy knees and that CCA would increase after MPFL reconstruction over the course of low knee flexion. Cohort study; Level of evidence, 2. In a prospective matched-paired cohort study, the CCA of 13 patients with low-flexion PFI was determined before and after MPFL reconstruction, and the data were compared with those of 13 healthy volunteers (controls). MRI was performed with the knee at 0°, 15°, and 30° of flexion in a custom-designed knee-positioning device. To suppress motion artifacts, motion correction was performed using a Moiré Phase Tracking system via a tracking marker attached to the patella. The CCA was calculated on the basis of semiautomatic cartilage and bone segmentation and registration. The CCA (mean ± SD) at 0°, 15°, and 30° of flexion for the control participants was 1.38 ± 0.62, 1.91 ± 0.98, and 3.68 ± 0.92 cm2, respectively. In patients with PFI, the CCA at 0°, 15°, and 30° of flexion was 0.77 ± 0.49, 1.26 ± 0.60, and 2.89 ± 0.89 cm2 preoperatively and 1.65 ± 0.55, 1.97 ± 0.68, and 3.52 ± 0.57 cm2 postoperatively. Patients with PFI exhibited a significantly reduced preoperative CCA at all 3 flexion angles when compared with controls (P ≤ .045 for all). Postoperatively, there was a significant increase in CCA at 0° of flexion (P = .001), 15° of flexion (P = .019) and 30° of flexion (P = .026). There were no significant postoperative differences in CCA between patients with PFI and controls at any flexion angle. Patients with low-flexion patellar instability showed a significant reduction in patellofemoral CCA at 0°, 15°, and 30° of flexion. MPFL reconstruction increased the contact area significantly at all angles.

Automated segmentation of palpebral fissures from eye videography using a texture fusion neural network

Automated segmentation of palpebral fissures depicted on images of eye videography plays a crucial role in identifying blinks (especially incomplete blinks) and detecting some eye disorders (e.g., dry or tired eyes). To this end, we propose a texture fusion segmentation network (called TF-Net) on the basis of the popular U-Net for automatically segmenting palpebral fissures from eye videography. The proposed network is constructed by defining two novel subtraction convolutional blocks and introducing them into the U-Net. These subtraction blocks are able to suppress motion artifacts and complex background in some degrees and enhance the sensitivity of the proposed network to objects of interest, as compared to the U-Net. Extensive experiments on our collected blink images from eye videography and three public datasets demonstrated that the proposed network obtained, on average, the Dice score (DS), Jaccard similarity (JS), and Hausdorff distance (HD) of 0.9445, 0.9099, and 5.3657, respectively, on the blink image dataset, and 0.8364, 0.7434, and 4.5745 for 14 objects of interest on three public datasets, which surpassed the U-Net and its multiple variants.

Change in Descriptive Kinematic Parameters of Patients with Patellofemoral Instability When Compared to Individuals with Healthy Knees—A 3D MRI In Vivo Analysis

Patellofemoral instability (PFI) leads to chronic knee pain, reduced performance and chondromalacia patellae with consecutive osteoarthritis. Therefore, determining the exact patellofemoral contact mechanism, as well as the factors leading to PFI, is of great importance. The present study compares in vivo patellofemoral kinematic parameters and the contact mechanism of volunteers with healthy knees and patients with low flexion patellofemoral instability (PFI). The study was performed with a high-resolution dynamic MRI. In a prospective cohort study, the patellar shift, patella rotation and the patellofemoral cartilage contact areas (CCA) of 17 patients with low flexion PFI were analyzed and compared with 17 healthy volunteers, matched via the TEA distance and sex, in unloaded and loaded conditions. MRI scans were carried out for 0°, 15° and 30° knee flexion in a custom-designed knee loading device. To suppress motion artifacts, motion correction was performed using a moiré phase tracking system with a tracking marker attached to the patella. The patellofemoral kinematic parameters and the CCA was calculated on the basis of semi-automated cartilage and bone segmentation and registrations. Patients with low flexion PFI showed a significant reduction in patellofemoral CCA for 0° (unloaded: p = 0.002, loaded: p = 0.004), 15° (unloaded: p = 0.014, loaded: p = 0.001) and 30° (unloaded: p = 0.008; loaded: p = 0.001) flexion compared to healthy subjects. Additionally, patients with PFI revealed a significantly increased patellar shift when compared to volunteers with healthy knees at 0° (unloaded: p = 0.033; loaded: p = 0.031), 15° (unloaded: p = 0.025; loaded: p = 0.014) and 30° flexion (unloaded: p = 0.030; loaded: p = 0.034) There were no significant differences for patella rotation between patients with PFI and the volunteers, except when, under load at 0° flexion, PFI patients showed increased patellar rotation (p = 0.005. The influence of quadriceps activation on the patellofemoral CCA is reduced in patients with low flexion PFI. Patients with PFI showed different patellofemoral kinematics at low flexion angles in both unloaded and loaded conditions compared to volunteers with healthy knees. Increased patellar shifts and decreased patellofemoral CCAs were observed in low flexion angles. The influence of the quadriceps muscle is diminished in patients with low flexion PFI. Therefore, the goal of patellofemoral stabilizing therapy should be to restore a physiologic contact mechanism and improve patellofemoral congruity for low flexion angles.

Paper] A 2-Tap 4-Phase Indirect Time-of-Flight Ranging Method using Half-Pulse Modulation for Depth Precision Enhancement and Sub-Frame Operation for Motion Artifact Suppression

Paper] A 2-Tap 4-Phase Indirect Time-of-Flight Ranging Method using Half-Pulse Modulation for Depth Precision Enhancement and Sub-Frame Operation for Motion Artifact Suppression

Motion robust magnetic resonance imaging via efficient Fourier aggregation.

We present a method for suppressing motion artifacts in anatomical magnetic resonance acquisitions. Our proposed technique, termed MOTOR-MRI, can recover and salvage images which are otherwise heavily corrupted by motion induced artifacts and blur which renders them unusable. Contrary to other techniques, MOTOR-MRI operates on the reconstructed images and not on k-space data. It relies on breaking the standard acquisition protocol into several shorter ones (while maintaining the same total acquisition time) and subsequent efficient aggregation in Fourier space of locally sharp and consistent information among them, producing a sharp and motion mitigated image. We demonstrate the efficacy of the technique on T2-weighted turbo spin echo magnetic resonance brain scans with severe motion corruption from both 3T and 7T scanners and show significant qualitative and quantitative improvement in image quality. MOTOR-MRI can operate independently, or in conjunction with additional motion correction methods.

Dual Wavelength Photoplethysmography Framework for Heart Rate Calculation.

The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and his coinvestigatorsused the noise components extracted from an infrared PPG signal to denoise a green PPG signal from which HR was extracted. Until now, this approach was only tested on "micro-motion" such as finger tapping. In this study, we extend this technique to allow accurate calculation of HR under high-intensity full-body repetitive "macro-motion". Our Dual Wavelength (DWL) framework was tested on PPG data collected from 14 human participants while running on a treadmill. The DWL method showed the following attributes: (1) it performed well under high-intensity full-body repetitive "macro-motion", exhibiting high accuracy in the presence of motion artifacts (as compared to the leading accelerometer-dependent HR calculation techniques TROIKA and JOSS); (2) it used only PPG signals; auxiliary signals such as accelerometer signals were not needed; and (3) it was computationally efficient, hence implementable in wearable devices. DWL yielded a Mean Absolute Error (MAE) of 1.22|0.57 BPM, Mean Absolute Error Percentage (MAEP) of 0.95|0.38%, and performance index (PI) (which is the frequency, in percent, of obtaining an HR estimate that is within ±5 BPM of the HR ground truth) of 95.88|4.9%. Moreover, DWL yielded a short computation period of 3.0|0.3 s to process a 360-second-long run.

Analysis of photoplethysmogram signal to estimate heart rate during physical activity using fractional fourier transform – A sampling frequency independent and reference signal-less method

Background and objectiveAcquiring accurate and reliable health information using a PPG signal in wearable devices requires suppressing motion artifacts. This paper presents a method based on the Fractional Fourier transform (FrFT) to effectively suppress the motion artifacts in a Photoplethysmogram (PPG) signal for an accurate estimation of heart rate (HR). MethodsBy analyzing various PPG signals recorded under various physiological conditions and sampling frequencies, the proposed work determines an optimal value of the fractional order of the proposed FrFT. The proposed FrFT-based algorithm separates the motion artifacts component from the acquired PPG signal. Finally, the HR estimation accuracy during the strong motion artifact-affected windows is improved using a post-processing technique. The efficacy of the proposed method is evaluated by computing the root mean square error (RMSE). ResultsThe performance of the proposed algorithm is compared with methods in recent studies using test and training datasets from the IEEE Signal Processing Cup (SPC). The proposed method provides the mean absolute error of 1.88 beats per minute (BPM) on all twenty-three recordings. ConclusionsThe proposed method uses the Fourier method in the fractional domain. A noisy signal is rotated into an intermediate plane between the time and frequency domains to separate the signal from the noise. The algorithm incorporates FrFT analysis to suppress motion artifacts from PPG signals to estimate HR accurately. Further, a post-processing step is used to track the HR for accurate and reliable HR estimation. The proposed FrFT-based algorithm doesn't require additional reference accelerometers or hardware to estimate HR in real-time. The noise and signal separation is optimum for a fractional order (a) value in the vicinity of 0.6. The optimized value of fractional order is constant irrespective of the physical activity and sampling frequency.

Quality assessment of cardiac magnetic resonance myocardial scar imaging prior to ventricular arrhythmia ablation

High-resolution scar characterization using late gadolinium enhancement cardiac magnetic resonance imaging (LGE-CMR) is useful for guiding ventricular arrhythmia (VA) treatment. However, imaging study quality may be degraded by breath-holding difficulties, arrhythmias, and implantable cardioverter-defibrillators (ICDs). We evaluated the effect of image quality on left ventricle (LV) base to apex scar interpretation in pre-VA ablation LGE-CMR. 43 consecutive patients referred for VA ablation underwent gradient-recalled-echo LGE-CMR. In ICD patients (n = 24), wide-bandwidth inversion-recovery suppressed ICD artifacts. In non-ICD patients, single-shot steady-state free-precession LGE-CMR could also be performed to reduce respiratory motion/arrhythmia artifacts. Study quality was assessed for adequate/limited scar interpretation due to cardiac/respiratory motion artifacts, ICD-related artifacts, and image contrast. 28% of non-ICD patients had studies where image quality limited scar interpretation in at least one image compared to 71% of ICD patient studies (p = 0.012). A median of five image slices had limited quality per ICD patient study, compared to 0 images per non-ICD patient study. Poorer quality in ICD patients was largely due to motion-related artifacts (54% ICD vs 6% non-ICD studies, p = 0.001) as well as ICD-related image artifacts (25% of studies). In VA ablation patients with ICDs, conventional CMR protocols frequently have image slices with limited scar interpretation, which can limit whole-heart scar assessment. Motion artifacts contribute to suboptimal image quality, particularly in ICD patients. Improved methods for motion and ICD artifact suppression may better delineate high-resolution LGE scar features of interest for guiding VA ablation.